WhatsApp for Business

So... you need to get word out quickly to your staff and you don't want to send emails or text messages to their phones. Have no fear! WhatsApp has come to the rescue.

Step 1
Go to WhatsApp on your phone, if you don't have, Google and install it on your phone. The software will go through your Contacts, quickly showing you, those that use the app already.
Click on the "Menu Button" as shown in the photo.

This reveals the menu as shown in the photo below. Select "WhatsApp Web" enclosed in the red rectangle.

Clicking it, reveals  a page as shown below in the photo, which happens to be a QR scanner to scan QR code on WhatsApp website.

Step 2
Go to your web browser and type in "https://www.whatsapp.com/" in the urlbar, which takes you to the webpage as shown below.

Click on "WHATSAPP WEB" just like you did on the phone, which takes you to the page as shown below, which shows the QR code that you will scan with your phone to get connected.

Scan the code to connect.

Step 3
Voila!!, we are connected, as shown in the photo below.

The magic weapon is the "New group" function as shown in the photo below.

With this function, you can create groups and add contacts that you wish to be part of the group. As an example, for a minicab business, there can be a group called "All Drivers" to send a job out to everyone. There can be a group called "East" for everyone that lives east of town. Sending message is done by clicking on contact and typing in the textbox ( like the regular WhatsApp on the phone) , just happens to be done on a computer this time instead of a mobile phone.

Getting word out about offers and new products is another good use of this app. One can attach scanned copies of flyers and send out customers. The constraint of typing  on a mobile phone keyboard has been taken out and there is no limit to what can be done with software.

Enjoy  using WhatsApp for your business.

Mail merge with Email accounts

We had done a blog about how to read emails from multiple account all once. Click here to have a quick look. We also introduced everyone to an email client called Thunderbird.

Investigating further, we discovered that a lot more can be done with this amazing add-on in Thunderbird. Assuming there's a need to send personalised email to a lot people and we want to avoid the trouble of fixing the recipients' names one after another, we mail merge the email by using a standard message and fixing the names and titles of each recipients before sending. Mail merge was made popular with the popular word processing software Microsoft Word. The feature is used to send the same email to lot of people but making contents of the email specific to each recipient.

Email attachment for each recipients can also be sent with emails. The process works using a database or a list some sort  and fixing the name, title or information specific for each recipient.  The source( list or database) has to be in a CSV( Comma Separated Value) format. The software scans the source attaching specific record for each recipient and attaches it to fields in a squiggly bracket in an email template.

This feature does not come "out of the box" for Thunderbird email client, rather it comes as an extension or add-on to the software. To get this extension, one has to go to the Tool feature in the menu and select "Add-ons", just as shown in the picture below.

Selecting "Add-ons" will take one to the page that looks as shown below:

Click on "Get Add-ons" option just as in the photo and then do a search for "mail merge" and then install the add-on, it's ready to use once installation is completed.

To use the add-on, select "New" and "Message" from the "File" menu, just as shown in the image below, which is the usual step to take when sending an email.

A new window opens up, where email address, subject and the message are typed in. To activate mail merge, it has to be selected from the "File" menu of this new window,as shown in the diagram below.

Another window opens up, this window will point the template to the source where records will be looked up and attached to the template. This windows also has features that will enable users to make attachments,decide when the mail will be sent, parse source data ( in cases where delimiters other comma has been used). An image of the window is shown below.

This add-on is so good that it can populate email addresses from the source, typing mail addresses in simple as enclosing the "Email" field in squiggly brackets and it's typed in.

Enjoy the add-on.

Watson Analytics 2.0

Continuing from where we left off the last time. We covered two out of the five section of the software indicated in the diagram below by the bits in grey. So we are discussing 'Assemble' to 'Refine'.

Assemble: With 'Assemble' making a dashboard has never been easier. The software has reduced making of charts to a  simple 'drag and drop' process. The section has templates and layouts that users can use to make all kinds of dashboards. Once the template and layout is selected, fields are simply dragged into axes and filters to make graphs as shown in the diagram below.

There are several options of charts that can be made, it is just as simple as clicking on the chart that meets the objective and the job is done. Below is a diagram showing the types of charts available.

Social Media: This section scrapes social media for data based on criteria specified by the user. The criteria will include fields like language, source,The data collected can analyzed by topics,theme, geography,sentiment, active authors and demographics. This tool is useful for finding out the buzz around a service (or product or may be even a topic) in social media.  The software gives clues from data generated around the chosen topic in form of a word cloud, just like in the diagram.

Users can include these clues as  terms closely related to the search topic to get better results, they can also break down the topic by specifying a criteria of some sort, this is known as a 'Theme' in Watson Analytics. After the search is all sorted, a dataset is generated which can now be analyzed.

The diagram above shows a 'Sentiment' chart from the data collected.  The dataset fields are displayed at the bottom of the chart. Other information like topics, themes, languages, dates, documents and mentions are displayed at the top. All these details will specify the search and scrape criteria for the data. Users can change charts by using the chart criteria to display charts for criteria that was earlier specified.

Refine: Watson Analytics assesses data before use, result of assessment are usually displayed on the tile that links to the data as shown in the diagram below.

 It assesses data for missing values and inconsistencies that may affect use of the data and then awards marks. 100 for data that is consistent and has no missing values. The 'Refine' section does exactly what it says on the box. It is used for cleaning, munging and editing of data. The section can enable users to group data, form hierarchy, do calculations. Users can also search data, include/exclude rows and columns.  Each column is assessed , marks and charts are displayed showing quality of data.

There is still much to learn in Watson Analytics, the blog is just a headstart. As always, there's more information on the internet.

Give Watson Analytics a try. Thanks for reading.

Introducing Watson Analytics

Watson Analytics

So you have 100,000 rows of data to analyze, you cannot afford to hire a Data Scientist and you do not have the required skills.Have no fear, IBM Watson Analytics have come to the rescue.

Watson Analytics is IBM's attempt to replicate what Apple has done in computing to data science. They have managed to make the subject simple enough for mere mortals (with little or no knowledge of Data Science) like you and me to understand.

The software has sections that it uses to work on data just as shown in the image above, they are namely:

Explore: This section deals with data exploration, which is just having a feel of data in an attempt to find out what the data is all about. The section has charts that will help provide insight as to what the data is all about. It's an array of charts options that will paint a good picture of the data. This section allows users to probe data for insights that will inform decision. Once the data is uploaded and refined, Watson dissects data and comes up questions that users might be interested in finding answers to.

 Clicking on the question that best matches objectives, will reveal detailed answers with charts. The software will produce lots of graphs pertaining to the question asked. Clicking on a graph reveals a more detailed information. The image below shows graphs made from the data we tested with.

Predict: This section is basically linear regression in graphic user interface. It users predict values using variables in the datasets. Dependent variables are predicted using independent variables. Watson examines the quality of the dataset and scores it. This enables users to make changes that ensures accurate predictions. The software then explains that datasets using all sorts of graphs and tables showing things like skewness of data, Outliers, Box graphs etc. For users that are novices in linear regression (which is most of us), Watson is able to point out variables that predict other variables. In my dataset,  Item variable drives Unit cost, as shown below.

It also shows field associations , which is another way of showing which variables are correlated just as shown below.

 It also shows degree  and accuracy of predictability in a circular diagram just like the one shown below. It also offers alternatives where necessary, like in my dataset Rep provides a better prediction for item, with predictive strength of 99.6%

With these kind of information, users now know what  variables to tweak to get results from other variables. Will discuss other sections in subsequent blogs.


 Enjoy Watson.

Filtering in R

Filtering

In spreadsheets, filtering is done by clicking on 'Filters' found in the Data menu. Filters selects data that meets a specified criteria. Usually a drop-down box is attached to a column, unique values of all the data in the column appears in the drop-down box. Any value selected will make the software to display occurrences of the number selected, excluding all other values. In R, filtering is done by:
Slicing: Data once read into R software, can sliced to be get bits and pieces that are needed. As discussed from the previous blog, the nearest representation of tabular data (with rows and columns) is a dataframe.  Using RStudio, we read sample data into R in the previous blog. To get a sense of what the data looks like, the 'str' function is used. It stands for structure. Running the command in the console produces:

From the results, we can see that the data has 43 observations of 7 variables, each variable represents a column, there is also the 'factor' term found in the results. In R, category data is called a factor, the categories are called levels. Since columns are variables in R, the sigil sign '$' is used,  which also appears in the results above. To display contents of Region column will mean running the command below

df$Region

Will appear in the console as shown below:

 The diagram shows that Region column has 3 categories of data namely Central, East, West. 

To filter (called slicing in R). The square brackets are used. The rows,columns and filter criteria are written inside the brackets. The syntax is as shown below:

The dataframe [rows{Filter criteria},column]

To illustrate with our data, filtering data from Central Region will mean running the command below:

df2[df2$Region=='Central',]

Will appear in the console as shown below:

The importance of ',' in the syntax cannot be overemphasized because it what determines if rows or columns will be selected. In the example above, the command is for rows to be selected. If columns are needed in the filter, the command will be

df2[df2$Region=='Central',c('OrderDate','Rep','Units')]

Will run in the console to produce:

 The data above shows data from the 'Central' region but with columns specified rather than the entire data.  The next blog will discuss Pivot tables in R.

Enjoy filtering in R

 

Moving to R (Part 2)

Opening a spreadsheet file in R
 

Opening a file in R is called reading the file, as R is a command-line programming language. R like any other programming language requires modules to get stuff done and opening (reading) a spreadsheet file will only happen when the right module is attached. A module is simply a portion of a computer program that can be used independently or with other modules to meet an objective.

 R Studio IDE

R Studio IDE shown above,has got to be the gold standard for what an R IDE should be. Shown below, it's got 4 windows namely:

1)Console: This window has the console which is where the compiler is, the compiler executes the R scripts

2)Workspace and history : This window has a directory-like container where resources connected to a task is kept. It also has a tab ' history 'where lines of codes compiled earlier are stored.

3)Files,plots,packages and help : This window has directory that shows all system files. that shows has a tab that shows graphs and another for packages. Packages are special R programs for doing lots of things.

4)Scripts and Data view: This window has tab for scripts and viewing data. Script written can be executed in the console. The data view tab is used for examining data.

Reading a spreadsheet file
There are several modules for reading spreadsheet file into R. This web page discusses it in details. Using R Studio, we will show how to read spreadsheet data into R. Below, are screenshots  of the windows mentioned above reading file into R.

Scripts and Data view

Above the Script view, script to read  spreadsheet data using two methods, Method 1 used the 'gdata' module using the 'require'  keyword and read.xls function is used to load the spreadsheet file into R. Method 2 involved saving the spreadsheet file as a text or csv (Comma Seperated Value) and using read.csv function, the data was read into R. Observe that each data read has been assigned a value using the '=' assignment operator. Also see two tabs representing views of the data read in. Scripts written in the script window are executed in the Console view by pressing a combination of keys on the keyboard. The keys are :

Ctrl+Shift + Enter

Console

 
Pressing the keys above, makes the console to compile any codes from the script. Any operation in IDE must be translated to a code in the console for it to work. An example selecting a data view tab will be translated to 'view(the data to be viewed)'. The other two windows do not have changes.

Aggregation, Charting, Pivot tables, Filtering and so on, and so forth can be done in R, we will explore further in the next blog.

Enjoy trying R out.

 

 

Moving over to R

The problem

Data comes in all shapes and sizes. Sometimes they come with some many columns that makes it difficult for one to make sense of . A special kind tool has to be used to work on the data, that tool is R.

R

R is an open source software used for analytics and statistic computing. It has become quite popular because its capabilities. The software is able to do almost everything a spreadsheet does and more.

Structure

R is structured differently from spreadsheets. In spreadsheet, cells stack up to make rows and columns and data is arranged in a tabular form. Not so with R, the closest thing to tabular data representation is called a Dataframe. The closest thing to a column in R is called a vector and for row is list. For example a named column in a spreadsheet is represented as :

In R it is :Tax <- c(34,45,60,70,90,30,45), the 'c' stands for concatenate, which is also a function in spreadsheet for combining data. A named row in a spreadsheet can be:

In R becomes: Tom <- c(Tom,25,London). So a vector contains the same data type while a list do not have the same datatype. 

Interface

Most spreadsheet software have a graphical user interface, that means that users interact with the program with a mouse,touchpad etc. R is a programming language, so interaction is command-line, users have to type in lines of code. R has a GUI module that has to be activated through commandline before use. It's called Rattle. Most R users use an IDE or editor of some sort to write code that R compiler will compile. A search on Google will list out most of them, but RStudio comes highly recommended. Spreadsheets operate on data in cells that stack up to make rows and columns, R being a programming language operates by using variables assigned to vectors and lists that stack up to make Dataframes. 

Assignment operators
People conversant with R must have come across this symbol '<-', it's called a left side assignment operator, it assigns data to names the R software can recognize and work with, just like the example shown earlier, repeated below as :

Tom <- c(Tom,25,London)

There is a right side assignment operator, which looks like this '->', which is just the reverse of the one above, shown below as :

c(Tom,25,London) -> Tom

Finally, the assignment operator used in most programming languages, the '=' sign. This operator is my favourite because it takes just one keystroke to reach unlike the rest two that takes 3 keystrokes (the two symbols + a shift key), shown below as :

Tom = c(Tom,25,London)

Let's stop here for now. There will be another blog on R that will discuss how to do most thing that spreadsheets do in R.

Why don't you give R a go ?